Towards real‐time through‐obstacle imaging based on compressed sensing for sparse objects
Bibliographic record
Abstract
Although significant progress has been made in microwave imaging, real‐time imaging, especially for objects behind walls or closed obstacles, remains a technical challenge. In this work, highly efficient imaging for complex‐structured objects surrounded by a closed obstacle was experimentally demonstrated. The imaging equations are derived based on a combination of the inverse‐scattering problem and the concept of compressed sensing. Making use of the spatial sparsity of objects and obstacles, the compressed imaging can be implemented using a time‐division multi‐antenna setup with reduced transmitting antennas. Owing to the spatial compressed sensing applied to the sparse imaging region and objects, the imaging time can be reduced by two orders of magnitude compared with the conventional twofold subspace‐based optimisation method with a comparable imaging quality. Taking advantage of the sparsity of the entire imaging area, objects with larger relative permittivity can also be reconstructed. The proposed method can be potentially used in applications such as security examination through boxes. It also provides a new clue for solving the practicability difficulty faced by existing microwave imaging systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".